Technological advances have lead to process-driven automated equipment that is increasingly complex. A tool system to accomplish a specific goal or perform a specific, highly technical process can commonly incorporate multiple functional elements to accomplish the goal or successfully execute the process, and various sensors that collect data to monitor the operation of the equipment. Such automated equipment can generate a large volume of data. Data can include substantial information related to a product or service performed as a part of the specific task, but it can also comprise sizable log information related to the execution of the process itself.
While modern electronic storage technologies can afford retaining constantly increasing quantities of data, utilization of the accumulated data remains far from optimal. Examination and interpretation of collected information generally requires human intervention, and while advances in computing power such as multiple-core processors, massively parallel platforms and processor grids, as well as advances in computing paradigms like object-oriented programming, modular code reuse, web based applications and more recently quantum computing, the processing of the collected data remains to be a non-autonomous, static programmatic enterprise wherein the data is operated upon. More importantly, in non-autonomous data processing, the data fails to drive the analysis process itself. As a consequence of such data processing paradigm, much of the rich relationships that can be present among data generated in automated equipment during a highly technical process can be unnoticed unless a specific analysis is designed and focused on a specific type of relationship. More importantly, emergent phenomena that can originate from multiple correlations among disparate data generated by disparate units in the equipment, and that can determine optimal performance of a complex automated tool or machine, can remain unnoticed.
In addition, the various correlations among data and variables associated with a process performed in a machine can deliver substantial information related to the actual operational performance of a set of tools or machines. It should be appreciated that specific calibration correlations can develop during synthetic operation of the set of tools, and disparate production correlations can develop as a result of the operation in production mode. The disparity in the correlations can arise from evolution or adjustment of a tool (e.g., wear and tear, fault(s) in operation such as utilization of an instrument outside prescribed conditions, etc.). Conventional systems and approaches that monitor performance of one or more instruments in a process typically utilize data that fails to capture and exploit such production correlations.